class_docstr: Large-scale distributed sampling face recognition training Module, of course sampling is an option,
  There will be no loss of accuracy in sampling in large-scale identities training tasks, uses only
  8 NVIDIA RTX2080Ti to complete classification tasks with 10 millions of identities, 64 NVIDIA
  RTX2080Ti can complete classification tasks with 100 million of identities.
  
  See the original paper:
  https://arxiv.org/abs/2203.15565
  
  Parameters
  ----------
  symbol: Symbol
      Backbone symbol.
  fc7_model: Object
      Object of margin loss.
  memory_bank: Memory bank Object.
      Object of memory bank, which maintain local class centers and their momentum.
  memory_optimizer: Optimizer object.
      The updater of memory bank, default is sgd optimizer.
  logger:
docstr: Initializes the parameters and auxiliary states.
  
  Parameters
  ----------
  initializer : Initializer
      Called to initialize parameters if needed.
  arg_params : dict
      If not ``None``, should be a dictionary of existing arg_params. Initialization
      will be copied from that.
  aux_params : dict
      If not ``None``, should be a dictionary of existing aux_params. Initialization
      will be copied from that.
  allow_missing : bool
      If ``True``, params could contain missing values, and the initializer will be
      called to fill those missing params.
  force_init : bool
      If ``True``, will force re-initialize even if already initialized.
  allow_extra : boolean, optional
      Whether allow extra parameters that are not needed by symbol.
      If this is True, no error will be thrown when arg_params or aux_params
      contain extra parameters that is not needed by the executor.
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code: def init_params(
      self,
      initializer,
      arg_params=None,
      aux_params=None,
      allow_missing=False,
      force_init=False,
      allow_extra=False,
  ):
      """Initializes the parameters and auxiliary states.
  
      Parameters
      ----------
      initializer : Initializer
          Called to initialize parameters if needed.
      arg_params : dict
          If not ``None``, should be a dictionary of existing arg_params. Initialization
          will be copied from that.
      aux_params : dict
          If not ``None``, should be a dictionary of existing aux_params. Initialization
          will be copied from that.
      allow_missing : bool
          If ``True``, params could contain missing values, and the initializer will be
          called to fill those missing params.
      force_init : bool
          If ``True``, will force re-initialize even if already initialized.
      allow_extra : boolean, optional
          Whether allow extra parameters that are not needed by symbol.
          If this is True, no error will be thrown when arg_params or aux_params
          contain extra parameters that is not needed by the executor.
      """
      # backbone
      self.backbone_module.init_params(
          initializer=initializer,
          arg_params=arg_params,
          aux_params=aux_params,
          allow_missing=allow_missing,
          force_init=force_init,
          allow_extra=allow_extra,
      )
  
imports: import logging
  import warnings
  from collections import namedtuple
  import horovod.mxnet
  import mxnet
  import mxnet.ndarray
  import numpy
  from default import config
  from optimizer import DistributedOptimizer
label_desc: Use image node_id 8 for calling the SampleDistributeModule obj's underlying member method code with example usage: obj.init_params(initializer, arg_params, aux_params, allow_missing, force_init, allow_extra) without return types